FAIAD: Feature Adaptive-based Image Anomaly Detection

Abstract

Abstract Image abnormality detection is a hot research topic in the field of data mining, and it has great application value in the fields of industrial appearance defect detection and medical image analysis. To address the problem of poor performance of anomaly detection models caused by incomplete feature extraction, we propose a feature-adaptive image anomaly detection model. FAIAD first trains the initial feature extraction model by pre-training the model. Then introduce feature adaptation methods to improve image feature extraction performance. The last step is to calculate the accuracy of image anomaly detection. In order to explore the feature extraction effects of different neural networks, this paper designs three kinds of backbone network comparison experiments. Experimenting on both Cifar-10 and Fashion-MNIST datasets, the accuracy of our model improved by 3.5% and 2.3%, respectively, compared to the baseline model. The experimental results show that combining pre-trained models with feature adaptation methods can effectively improve the performance of anomaly detection models.